This algorithm describes the ongoing resistant hypertension algorithm used in eMERGE, which was a network phenotype within the eMERGE-I and eMERGE-II sites.
This rheumatoid arthritis (RA) algorithm was created using a machine-learning logistic regression model.
To identify cases with auto-immune rheumatologic phenotye (for NT198) we request information about auto-antibody (whether it was tested and what the restults were) and drug information (whether it was prescribed) for each patients that is enrolled in eMERGE. We are requesting every mention of any of the expanded generic drugs.
Systemic Lupus Erythematosus (SLE) is a chronic, systemic autoimmune disease that can affect many parts of the body including skin, lungs, brain, heart, kidneys, joints, and blood vessels. SLE presentation can vary significantly between patients. Because of this, it can be challenging to identify a patient as having SLE. Between 300,000 and 2,000,000 people in the US are estimated to have SLE. Determination of an exact number of people affected is challenging as the disease is difficult to identify given the diverse presentations and the length of time it may take for symptoms to appear.
- The computable phenotype for the Sleep Apnea Patient Centered Outcomes Network uses existing and well established ICD codes for different types of sleep apnea including 327.23 (adult and pediatric obstructive sleep apnea), 780.51 (insomnia with sleep apnea), 780.53 (hypersomnia with sleep apnea), and 780.57 (unspecified sleep apnea).
Phenotype Description: Patients on statins for primary prevention who develop an AMI or 1st AMI.
Below are algorithms used to identify AMI and 1st AMI cohort at BioVU. If you have questions regarding any of the information presented on this page, you may contact either:
Wei-Qi Wei at email@example.com
Joshua Denny at firstname.lastname@example.org
We used Vanderbilt’s Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist.
This document describes the Stanford University algorithm to extract individuals with diabetes and the type of diabetes from electronic health records (EHRs). There are two main tasks of this phenotype development: 1) to extract patients with diabetes (gestational diabetes is excluded), and 2) to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Instead of identifying all diabetes cases, we aim to reduce the number of false positives in our diabetes cohort.